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The fairness‐accuracy Pareto front
Author(s) -
Wei Susan,
Niethammer Marc
Publication year - 2022
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11560
Subject(s) - computer science , scheme (mathematics) , pareto principle , mathematical optimization , multi objective optimization , pareto optimal , chebyshev filter , classifier (uml) , chebyshev polynomials , fairness measure , algorithm , artificial intelligence , mathematics , machine learning , mathematical analysis , computer vision , telecommunications , throughput , wireless
Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multiobjective optimization and seek the fairness‐accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so‐called linear scalarization scheme, which has severe limitations in recovering Pareto optimal solutions. We instead apply the Chebyshev scalarization scheme which is provably superior theoretically and no more computationally burdensome at recovering Pareto optimal solutions compared to the linear scheme.